class_docstr: Whitespace tokenization layer.
  
  Splits a string into substrings at ICU whitespace boundaries.
  
  Attributes:
    pad_value: if not None, performs the padding (using pad_value) at the
      inner-most dimension (i.e. token dimension) and outputs a padded dense
      tensor (default=None).
    squeeze_token_dim: Whether to squeeze the dimension added by tokenization.
      When this arg is set to False, the output will have an additional inner
      dimension added, containing the tokens in each string; when this arg is
      True, the layer will attempt to squeeze that dimension out. If you are
      passing one string per batch, you probably want to keep this as True; if
      you are passing more than one string per batch or are using this layer in
      a context like the Keras `TextVectorization` layer which expects a
      tf.strings.split()-stype output, this should be False. Defaults to True.
code: def __init__(self, pad_value=None, squeeze_token_dim=True, **kwargs):
      tokenizer_fn = whitespace_tokenizer.WhitespaceTokenizer()
      super(WhitespaceTokenizer, self).__init__(
          tokenizer_instance=tokenizer_fn,
          squeeze_token_dim=squeeze_token_dim,
          pad_value=pad_value,
          **kwargs,
      )
  
imports: import os
  import tensorflow
  from tensorflow.python.ops import lookup_ops
  from tensorflow.python.ops.ragged import ragged_conversion_ops
  from tensorflow_text.python.ops import unicode_script_tokenizer
  from tensorflow_text.python.ops import whitespace_tokenizer
  from tensorflow_text.python.ops import wordpiece_tokenizer
label_desc: Use image node_id 1 to create a new WhitespaceTokenizer object from inherited base classes: TokenizerBase with example: obj = WhitespaceTokenizer(pad_value, squeeze_token_dim)